M
Michael I. Jordan
Researcher at University of California, Berkeley
Publications - 1110
Citations - 241763
Michael I. Jordan is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Inference. The author has an hindex of 176, co-authored 1016 publications receiving 216204 citations. Previous affiliations of Michael I. Jordan include Stanford University & Princeton University.
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Prediction-Powered Inference
TL;DR: In this article , a framework for performing valid statistical inference when an experimental data set is supplemented with predictions from a machine-learning system is introduced, without making any assumptions on the machine learning algorithm that supplies the predictions.
Posted Content
Manifold Learning via Manifold Deflation.
Daniel Ting,Michael I. Jordan +1 more
TL;DR: An embedding method for Riemannian manifolds is derived that iteratively uses single-coordinate estimates to eliminate dimensions from an underlying differential operator, thus "deflating" it and proving its consistency when the coordinates converge to true coordinates.
Journal ArticleDOI
Pretreatment Human Immunodeficiency Virus (HIV) Drug Resistance Among Treatment-Naive Infants Newly Diagnosed With HIV in 2016 in Namibia: Results of a Nationally Representative Study
Michael I. Jordan,Leonard Bikinesi,Laimi S.N. Ashipala,Nicholus Mutenda,Mary N. A. Brantuo,Gillian Hunt,Andreas N Shiningavamwe,Gram Mutandi,Anita Beukes,Suzanne S. Beard,Katherine A. Battey,Eric J. Dziuban,Elliot Raizes,Paul C. Adjei,Alice M. Tang,Amalia Giron,Steven Y. Hong +16 more
TL;DR: The high level of EFV/NVP resistance is unsurprising; however, levels of ABC and TDF resistance are among the highest observed to date in infants in sub-Saharan Africa and underscore the need for antiretroviral therapy optimization and prompt management of high viral loads in infants and pregnant and breastfeeding women.
Statistical Monitoring + Predictable Recovery = Self-*
TL;DR: The hope is that this approach will enable inew science in the design of self-managing systems by allowing the rapid and widespread application of statistical learning theory techniques (SLT) to problems of system dependability.
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Predictors of loss to follow-up from HIV antiretroviral therapy in Namibia
Steven Y. Hong,Anna Winston,Nicholus Mutenda,Ndapewa Hamunime,Tuhin Suvra Roy,Christine Wanke,Alice M. Tang,Michael I. Jordan +7 more
TL;DR: Interventions to reduce LTFU should target young men, particularly those who report difficulty leaving work or home to attend clinic and are on an efavirenz-based regimen, and requires research to elucidate context-specific factors.